SIMON: Open-Source Knowledge Discovery Platform

被引:10
|
作者
Tomic, Adriana [1 ,2 ]
Tomic, Ivan [3 ]
Waldron, Levi [4 ,5 ]
Geistlinger, Ludwig [4 ,5 ]
Kuhn, Max [6 ]
Spreng, Rachel L. [7 ]
Dahora, Lindsay C. [7 ]
Seaton, Kelly E. [7 ]
Tomaras, Georgia [7 ]
Hill, Jennifer [1 ]
Duggal, Niharika A. [8 ]
Pollock, Ross D. [9 ]
Lazarus, Norman R. [9 ]
Harridge, Stephen D. R. [9 ]
Lord, Janet M. [8 ,10 ,11 ]
Khatri, Purvesh [2 ,12 ]
Pollard, Andrew J. [1 ]
Davis, Mark M. [2 ,13 ,14 ]
机构
[1] Univ Oxford, Dept Paediat, Oxford Vaccine Grp, Oxford, England
[2] Stanford Univ, Sch Med, Inst Immun Transplantat & Infect, Stanford, CA 94305 USA
[3] Univ Oxford, Nuffield Dept Womens & Reprod Hlth, Deep Med, Oxford, England
[4] CUNY, Grad Sch Publ Hlth & Hlth Policy, New York, NY 10021 USA
[5] CUNY, Inst Implementat Sci & Populat Hlth, New York, NY 10021 USA
[6] PBC, RStudio, Boston, MA USA
[7] Duke Univ, Duke Human Vaccine Inst, Durham, NC USA
[8] Univ Birmingham, MRC Versus Arthrit Ctr Musculoskeletal Ageing Res, Inst Inflammat & Ageing, Res Labs, Birmingham, W Midlands, England
[9] Kings Coll London, Ctr Human & Appl Physiol Sci, London, England
[10] Univ Hosp Birmingham NHS Fdn Trust, NIHR Birmingham Biomed Res Ctr, Birmingham, W Midlands, England
[11] Univ Birmingham, Birmingham, W Midlands, England
[12] Stanford Univ, Ctr Biomed Informat Res, Dept Med, Stanford, CA 94305 USA
[13] Stanford Univ, Sch Med, Dept Microbiol & Immunol, Stanford, CA 94305 USA
[14] Stanford Univ, Howard Hughes Med Inst, Stanford, CA 94305 USA
来源
PATTERNS | 2021年 / 2卷 / 01期
基金
欧盟地平线“2020”;
关键词
MACHINE LEARNING APPLICATIONS; SALMONELLA-TYPHI; MASS CYTOMETRY; EXPRESSION; PREDICTION; VACCINE; IMMUNE;
D O I
10.1016/j.patter.2020.100178
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of biological datasets, but the necessarily sophisticated programming skills and indepth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software, SIMON, to facilitate the application of 180+ state-of-the-art machine-learning algorithms to high-dimensional biomedical data. With an easy-touse graphical user interface, standardized pipelines, and automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.
引用
收藏
页数:9
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